Instructions to use bryanzhou008/vit-base-patch16-224-in21k-finetuned-inaturalist with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bryanzhou008/vit-base-patch16-224-in21k-finetuned-inaturalist with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="bryanzhou008/vit-base-patch16-224-in21k-finetuned-inaturalist") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("bryanzhou008/vit-base-patch16-224-in21k-finetuned-inaturalist") model = AutoModelForImageClassification.from_pretrained("bryanzhou008/vit-base-patch16-224-in21k-finetuned-inaturalist", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b2e1becbdcd8892cc0997e579b7ebe475568c65a7ab86c13d62e9d5a908c5dce
- Size of remote file:
- 5.3 kB
- SHA256:
- 963872fb6e4b8e19e08e8b7588df31a8a0b49bda3f697bc94c7c03bf82349564
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.